scLANE Simulation Study - Trajectory DE Method Comparison

UF Dept. of Biostatistics - Bacher Group

Libraries

library(dplyr)
library(ggplot2)
library(targets)

Helper Functions

source("R/functions_analysis.R")

Data

tar_load(metric_table_master)

Analysis

Reference Dataset

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ROC_AUC, y = SIM_REFERENCE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  facet_wrap(~MODEL_TYPE) + 
  ggridges::geom_density_ridges(alpha = 0.6, scale = 0.95, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent()) + 
  labs(y = "Reference Dataset", x = "ROC-AUC") + 
  theme_analysis() + 
  theme(legend.position = "none")

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = F_MEASURE, y = SIM_REFERENCE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  facet_wrap(~MODEL_TYPE) + 
  ggridges::geom_density_ridges(alpha = 0.6, scale = 0.95, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 0.1), 
                     limits = c(NA, 1)) + 
  labs(y = "Reference Dataset", x = "F-measure") + 
  theme_analysis() + 
  theme(legend.position = "none")

Runtime

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS), 
         RUNTIME_MINS = RUNTIME_HOURS * 60) %>% 
  ggplot(aes(x = N_CELLS, y = RUNTIME_MINS, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(accuracy = 1, suffix = "min")) + 
  labs(x = "Cells", y = "Runtime") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(RUNTIME_MINS = RUNTIME_HOURS * 60, 
         GENES_PER_MIN = N_GENES / RUNTIME_MINS) %>% 
  ggplot(aes(x = N_CELLS, y = GENES_PER_MIN, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_smooth(method = "lm", alpha = 0.25) + 
  scale_y_continuous(labels = scales::label_number(accuracy = 1)) + 
  labs(x = "Cells", y = "Genes per Minute") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 0, linewidth = 2)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(RUNTIME_MINS = RUNTIME_HOURS * 60, 
         GENES_PER_MIN = N_GENES / RUNTIME_MINS) %>% 
  ggplot(aes(x = GENES_PER_MIN, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 1)) + 
  labs(x = "Genes per Minute", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Memory Usage

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS), 
         MEM_USED_GB = MEM_USED / 1000) %>% 
  ggplot(aes(x = N_CELLS, y = MEM_USED_GB, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(suffix = "gb")) + 
  labs(x = "Cells", y = "Memory Usage") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(MEM_USED_GB = MEM_USED / 1000) %>% 
  ggplot(aes(x = MEM_USED_GB, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(suffix = "gb")) + 
  labs(x = "Memory Usage", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Predictive Performance

F-measure

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = F_MEASURE, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_number(accuracy = 0.1)) + 
  labs(x = "Cells", y = "F-measure") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = F_MEASURE, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_number(accuracy = 0.1)) + 
  labs(x = "F-measure", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Balanced Accuracy

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = BAL_ACCURACY, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Balanced Accuracy") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = BAL_ACCURACY, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Balanced Accuracy", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Recall

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = RECALL, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Recall") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = RECALL, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Recall", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

Accuracy

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = ACCURACY)) + 
  geom_boxplot(aes(color = MODEL_TYPE)) + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "Accuracy") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ACCURACY, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Accuracy", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

ROC-AUC

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = ROC_AUC, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "ROC-AUC") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = ROC_AUC, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "ROC-AUC", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM")) %>% 
  pull(ROC_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = 1 - specificity, y = sensitivity, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 0, y = 1, yend = 1, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + 
  labs(x = "1 - Specificity", 
      y = "Sensitivity", 
      color = "Cells", 
      title = "scLANE - GLM") + 
  theme_analysis() +  
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("tradeSeq")) %>% 
  pull(ROC_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = 1 - specificity, y = sensitivity, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 0, y = 1, yend = 1, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + 
  labs(x = "1 - Specificity", 
      y = "Sensitivity", 
      color = "Cells", 
      title = "tradeSeq") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

PR-AUC

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = N_CELLS, y = AUC_PR, color = MODEL_TYPE)) + 
  geom_boxplot() + 
  scale_y_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "Cells", y = "PR-AUC") + 
  theme_analysis() + 
  theme(legend.title = element_blank())

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM", "tradeSeq")) %>% 
  ggplot(aes(x = AUC_PR, color = MODEL_TYPE, fill = MODEL_TYPE)) + 
  geom_density(alpha = 0.3, linewidth = 1) + 
  scale_x_continuous(labels = scales::label_percent(accuracy = 1)) + 
  labs(x = "PR-AUC", y = "Density") + 
  theme_analysis() + 
  theme(legend.title = element_blank()) + 
  guides(color = guide_legend(override.aes = list(alpha = 1, color = "white", linewidth = 0.5)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("scLANE - GLM")) %>% 
  pull(PR_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = recall, y = precision, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 1, y = 1, yend = 0, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
  labs(x = "Recall", 
       y = "Precision", 
       color = "Cells", 
       title = "scLANE - GLM") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

filter(metric_table_master, 
       MODEL_TYPE %in% c("tradeSeq")) %>% 
  pull(PR_CURVE) %>% 
  purrr::reduce(rbind) %>% 
  left_join((distinct(metric_table_master, DATASET_NAME, N_CELLS)), 
            by = c("dataset" = "DATASET_NAME")) %>% 
  mutate(N_CELLS = round(N_CELLS, digits = -1), 
         N_CELLS = as.factor(N_CELLS)) %>% 
  ggplot(aes(x = recall, y = precision, group = dataset, color = N_CELLS)) + 
  facet_wrap(~paste0("Cells: ", N_CELLS)) + 
  geom_segment(x = 0, xend = 1, y = 1, yend = 0, color = "black", linetype = "dashed", size = 1) + 
  geom_line(size = 1, alpha = 0.8) + 
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_y_continuous(limits = c(0, 1), labels = scales::percent_format(accuracy = 1)) +
  labs(x = "Recall", 
       y = "Precision", 
       color = "Cells", 
       title = "tradeSeq") + 
  theme_analysis() + 
  guides(color = guide_legend(override.aes = list(linewidth = 2, alpha = 1)))

Session Info

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.3 (2024-02-29)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2024-10-07
 pandoc   2.9.2.1 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date (UTC) lib source
 abind                  1.4-8      2024-09-12 [2] CRAN (R 4.3.3)
 AnnotationDbi          1.62.2     2023-07-02 [2] Bioconductor
 backports              1.5.0      2024-05-23 [1] CRAN (R 4.2.3)
 base64url              1.4        2018-05-14 [1] CRAN (R 4.3.3)
 bigassertr             0.1.6      2023-01-10 [1] CRAN (R 4.3.2)
 bigparallelr           0.3.2      2021-10-02 [1] CRAN (R 4.3.2)
 bigstatsr              1.5.12     2022-10-14 [1] CRAN (R 4.3.2)
 Biobase              * 2.60.0     2023-04-25 [2] Bioconductor
 BiocGenerics         * 0.46.0     2023-04-25 [2] Bioconductor
 BiocParallel           1.34.2     2023-05-22 [2] Bioconductor
 Biostrings             2.68.1     2023-05-16 [2] Bioconductor
 bit                    4.5.0      2024-09-20 [2] CRAN (R 4.3.3)
 bit64                  4.5.2      2024-09-22 [2] CRAN (R 4.3.3)
 bitops                 1.0-8      2024-07-29 [2] CRAN (R 4.3.3)
 blob                   1.2.4      2023-03-17 [2] CRAN (R 4.3.1)
 boot                   1.3-31     2024-08-28 [4] CRAN (R 4.3.3)
 broom                * 1.0.6      2024-05-17 [1] CRAN (R 4.2.3)
 broom.mixed            0.2.9.5    2024-04-01 [1] CRAN (R 4.2.3)
 bslib                  0.8.0      2024-07-29 [2] CRAN (R 4.3.3)
 cachem                 1.1.0      2024-05-16 [2] CRAN (R 4.3.3)
 callr                  3.7.6      2024-03-25 [1] CRAN (R 4.2.3)
 caTools                1.18.3     2024-09-04 [2] CRAN (R 4.3.3)
 circlize               0.4.16     2024-02-20 [1] CRAN (R 4.3.2)
 class                  7.3-22     2023-05-03 [4] CRAN (R 4.3.1)
 cli                    3.6.2      2023-12-11 [1] CRAN (R 4.2.3)
 clue                   0.3-65     2023-09-23 [1] CRAN (R 4.3.2)
 cluster                2.1.6      2023-12-01 [4] CRAN (R 4.3.2)
 coda                   0.19-4.1   2024-01-31 [2] CRAN (R 4.3.2)
 codetools              0.2-20     2024-03-31 [4] CRAN (R 4.3.3)
 colorspace             2.1-1      2024-07-26 [2] CRAN (R 4.3.3)
 combinat               0.0-8      2012-10-29 [2] CRAN (R 4.3.1)
 ComplexHeatmap         2.16.0     2023-04-25 [1] Bioconductor
 cowplot                1.1.3      2024-01-22 [2] CRAN (R 4.3.3)
 crayon                 1.5.3      2024-06-20 [2] CRAN (R 4.3.3)
 data.table             1.16.0     2024-08-27 [2] CRAN (R 4.3.3)
 DBI                    1.2.3      2024-06-02 [2] CRAN (R 4.3.3)
 DelayedArray           0.26.7     2023-07-28 [2] Bioconductor
 devtools               2.4.5      2022-10-11 [1] CRAN (R 4.2.2)
 dials                * 1.2.1      2024-02-22 [1] CRAN (R 4.2.3)
 DiceDesign             1.10       2023-12-07 [1] CRAN (R 4.3.3)
 digest                 0.6.35     2024-03-11 [1] CRAN (R 4.2.3)
 doParallel             1.0.17     2022-02-07 [1] CRAN (R 4.2.3)
 doSNOW                 1.0.20     2022-02-04 [1] CRAN (R 4.3.2)
 dplyr                * 1.1.4      2023-11-17 [1] CRAN (R 4.2.3)
 edgeR                  3.42.4     2023-05-31 [2] Bioconductor
 ellipsis               0.3.2      2021-04-29 [2] CRAN (R 4.3.1)
 emmeans                1.10.2     2024-05-20 [1] CRAN (R 4.3.3)
 estimability           1.5.1      2024-05-12 [1] CRAN (R 4.3.3)
 evaluate               0.24.0     2024-06-10 [1] CRAN (R 4.3.3)
 fansi                  1.0.6      2023-12-08 [2] CRAN (R 4.3.1)
 farver                 2.1.2      2024-05-13 [1] CRAN (R 4.2.3)
 fastICA                1.2-4      2023-11-27 [1] CRAN (R 4.3.2)
 fastmap                1.2.0      2024-05-15 [2] CRAN (R 4.3.3)
 flock                  0.7        2016-11-12 [1] CRAN (R 4.3.2)
 forcats              * 1.0.0      2023-01-29 [1] CRAN (R 4.3.2)
 foreach                1.5.2      2022-02-02 [1] CRAN (R 4.2.3)
 fs                     1.6.4      2024-04-25 [2] CRAN (R 4.3.3)
 furrr                  0.3.1      2022-08-15 [1] CRAN (R 4.3.2)
 future               * 1.33.2     2024-03-26 [1] CRAN (R 4.2.3)
 future.apply           1.11.2     2024-03-28 [2] CRAN (R 4.3.3)
 future.callr         * 0.8.2      2023-08-09 [1] CRAN (R 4.2.3)
 gamlss                 5.4-22     2024-03-20 [1] CRAN (R 4.3.2)
 gamlss.data            6.0-6      2024-03-14 [1] CRAN (R 4.3.2)
 gamlss.dist            6.1-1      2023-08-23 [1] CRAN (R 4.3.2)
 geeM                   0.10.1     2018-06-18 [1] CRAN (R 4.3.2)
 generics               0.1.3      2022-07-05 [1] CRAN (R 4.2.0)
 GenomeInfoDb         * 1.36.4     2023-10-02 [2] Bioconductor
 GenomeInfoDbData       1.2.10     2023-07-26 [2] Bioconductor
 GenomicRanges        * 1.52.1     2023-10-08 [2] Bioconductor
 GetoptLong             1.0.5      2020-12-15 [1] CRAN (R 4.3.2)
 ggplot2              * 3.5.1      2024-04-23 [1] CRAN (R 4.2.3)
 ggridges               0.5.6      2024-01-23 [2] CRAN (R 4.3.3)
 glm2                 * 1.2.1      2018-08-11 [1] CRAN (R 4.3.2)
 glmmTMB                1.1.9      2024-03-20 [1] CRAN (R 4.3.3)
 GlobalOptions          0.1.2      2020-06-10 [1] CRAN (R 4.3.2)
 globals                0.16.3     2024-03-08 [2] CRAN (R 4.3.3)
 glue                   1.8.0      2024-09-30 [2] CRAN (R 4.3.3)
 GO.db                  3.17.0     2023-08-11 [2] Bioconductor
 gower                  1.0.1      2022-12-22 [2] CRAN (R 4.3.1)
 GPfit                  1.0-8      2019-02-08 [1] CRAN (R 4.3.3)
 gplots                 3.1.3.1    2024-02-02 [2] CRAN (R 4.3.3)
 graph                  1.78.0     2023-04-25 [1] Bioconductor
 gridExtra              2.3        2017-09-09 [2] CRAN (R 4.3.1)
 gtable                 0.3.5      2024-04-22 [2] CRAN (R 4.3.3)
 gtools                 3.9.5      2023-11-20 [2] CRAN (R 4.3.1)
 hardhat                1.4.0      2024-06-02 [1] CRAN (R 4.3.3)
 highr                  0.11       2024-05-26 [2] CRAN (R 4.3.3)
 hms                    1.1.3      2023-03-21 [2] CRAN (R 4.3.1)
 htmltools              0.5.8.1    2024-04-04 [1] CRAN (R 4.2.3)
 htmlwidgets            1.6.4      2023-12-06 [2] CRAN (R 4.3.3)
 httpuv                 1.6.15     2024-03-26 [2] CRAN (R 4.3.3)
 httr                   1.4.7      2023-08-15 [2] CRAN (R 4.3.3)
 igraph                 2.0.3      2024-03-13 [1] CRAN (R 4.2.3)
 infer                * 1.0.7      2024-03-25 [1] CRAN (R 4.2.3)
 ipred                  0.9-15     2024-07-18 [2] CRAN (R 4.3.3)
 IRanges              * 2.34.1     2023-06-22 [2] Bioconductor
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 jquerylib              0.1.4      2021-04-26 [2] CRAN (R 4.3.1)
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 KEGGREST               1.40.1     2023-09-29 [2] Bioconductor
 KernSmooth             2.23-24    2024-05-17 [4] CRAN (R 4.3.3)
 knitr                  1.48       2024-07-07 [2] CRAN (R 4.3.3)
 labeling               0.4.3      2023-08-29 [2] CRAN (R 4.3.1)
 Lamian               * 0.99.2     2024-03-20 [1] Github (Winnie09/Lamian@7d5a8ff)
 later                  1.3.2      2023-12-06 [2] CRAN (R 4.3.3)
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 limma                  3.56.2     2023-06-04 [2] Bioconductor
 listenv                0.9.1      2024-01-29 [2] CRAN (R 4.3.3)
 lme4                   1.1-35.5   2024-07-03 [2] CRAN (R 4.3.3)
 locfit                 1.5-9.10   2024-06-24 [2] CRAN (R 4.3.3)
 lubridate            * 1.9.3      2023-09-27 [2] CRAN (R 4.3.3)
 magrittr             * 2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
 MASS                   7.3-60     2023-05-04 [4] CRAN (R 4.3.1)
 Matrix                 1.6-5      2024-01-11 [1] CRAN (R 4.2.3)
 matrixcalc             1.0-6      2022-09-14 [1] CRAN (R 4.3.2)
 MatrixGenerics       * 1.12.3     2023-07-30 [2] Bioconductor
 matrixStats          * 1.4.1      2024-09-08 [2] CRAN (R 4.3.3)
 mclust                 6.1.1      2024-04-29 [2] CRAN (R 4.3.3)
 memoise                2.0.1      2021-11-26 [2] CRAN (R 4.3.1)
 mgcv                   1.9-1      2023-12-21 [4] CRAN (R 4.3.2)
 mime                   0.12       2021-09-28 [2] CRAN (R 4.3.1)
 miniUI                 0.1.1.1    2018-05-18 [2] CRAN (R 4.3.1)
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 nanonext               1.1.0      2024-06-04 [1] CRAN (R 4.3.3)
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 processx               3.8.4      2024-03-16 [2] CRAN (R 4.3.3)
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 purrr                * 1.0.2      2023-08-10 [1] CRAN (R 4.2.3)
 qs                   * 0.26.3     2024-05-16 [1] CRAN (R 4.3.3)
 R6                     2.5.1      2021-08-19 [2] CRAN (R 4.3.1)
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 RColorBrewer           1.1-3      2022-04-03 [2] CRAN (R 4.3.1)
 Rcpp                   1.0.13     2024-07-17 [2] CRAN (R 4.3.3)
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 RcppParallel           5.1.7      2023-02-27 [1] CRAN (R 4.3.2)
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 readr                * 2.1.5      2024-01-10 [2] CRAN (R 4.3.3)
 recipes              * 1.0.10     2024-02-18 [1] CRAN (R 4.2.3)
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 rhdf5                  2.44.0     2023-04-25 [2] Bioconductor
 rhdf5filters           1.12.1     2023-04-30 [2] Bioconductor
 Rhdf5lib               1.22.1     2023-09-10 [2] Bioconductor
 rjson                  0.2.23     2024-09-16 [2] CRAN (R 4.3.3)
 rlang                  1.1.4      2024-06-04 [1] CRAN (R 4.3.3)
 rmarkdown            * 2.27       2024-05-17 [1] CRAN (R 4.3.3)
 rpart                  4.1.23     2023-12-05 [4] CRAN (R 4.3.2)
 rsample              * 1.2.1      2024-03-25 [1] CRAN (R 4.2.3)
 RSQLite                2.3.7      2024-05-27 [2] CRAN (R 4.3.3)
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 scattermore            1.2        2023-06-12 [2] CRAN (R 4.3.1)
 scLANE               * 0.8.4      2024-10-05 [1] Github (jr-leary7/scLANE@d1eb84d)
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 SparseM                1.84-2     2024-07-17 [2] CRAN (R 4.3.3)
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 stringi                1.8.4      2024-05-06 [2] CRAN (R 4.3.3)
 stringr              * 1.5.1      2023-11-14 [2] CRAN (R 4.3.2)
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 tibble               * 3.2.1      2023-03-20 [2] CRAN (R 4.3.1)
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 tidyverse            * 2.0.0      2023-02-22 [1] CRAN (R 4.2.3)
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 topGO                  2.52.0     2023-04-25 [1] Bioconductor
 tradeSeq             * 1.14.0     2023-04-25 [1] Bioconductor
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 tune                 * 1.2.0      2024-03-20 [1] CRAN (R 4.2.3)
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 workflows            * 1.1.4      2024-02-19 [1] CRAN (R 4.2.3)
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 xtable                 1.8-4      2019-04-21 [2] CRAN (R 4.3.1)
 XVector                0.40.0     2023-04-25 [2] Bioconductor
 yaml                   2.3.10     2024-07-26 [2] CRAN (R 4.3.3)
 yardstick            * 1.3.1      2024-03-21 [1] CRAN (R 4.2.3)
 zlibbioc               1.46.0     2023-04-25 [2] Bioconductor

 [1] /home/j.leary/r_packages_default
 [2] /usr/local/lib/R/site-library
 [3] /usr/lib/R/site-library
 [4] /usr/lib/R/library

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